Batting Simulator Exit Velocity Measurement and Outcome Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional batting simulators in baseball facilities lack a mechanism to quantitatively measure the outcome of batted balls, such as whether a hit is a line drive double or fly ball, which hinders coaches' ability to assess player progress and improve hitting techniques.
Innovation Solution
A batting simulator system that measures exit velocity and trajectory of batted balls, combines this data with system performance metrics like virtual fielder and environmental factors to provide real-time outcomes, and stores results for analysis, allowing coaches to optimize swing mechanics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional batting simulators use optical sensors to detect batted ball trajectory, then ball motion can be measured, but the system cannot quantitatively measure the end result of the batted ball (e.g., whether it is a line drive double or fly ball)
Solution Approach 1:
The system divides the measurement process into two distinct segments: (1) optical sensors measure ball motion parameters (exit velocity, launch angle, horizontal/vertical distance), and (2) a separate computational module processes these parameters against a database of real-life pitch outcomes to determine the qualitative hit result. This segmentation allows each component to specialize, with sensors providing precise motion data and the computational module providing contextual interpretation.
Solution Approach 2:
The patent introduces an intermediary computational module that acts as a bridge between the raw sensor data and the meaningful hit outcome assessment. This intermediary takes the measured ball motion parameters, combines them with system performance metrics (fielder reaction time, running speed, arm strength) and environmental factors, then queries a database of real-life outcomes to determine whether the ball is a ground out, fly ball, home run, etc.
2Loss of information
If coaches manually chart batting results, then no quantitative measurement of hit outcomes is available, but the system is simple and requires no complex hardware
Solution Approach 1:
The system employs self-service through automated data collection and analysis. Optical sensors automatically track ball motion, the computational module automatically processes the data against performance metrics and environmental factors, and the system automatically queries the database to determine hit outcomes. This eliminates the need for manual charting by coaches while providing comprehensive quantitative assessment without requiring complex hardware intervention.
Solution Approach 2:
The patent replaces the manual mechanical charting system with an automated optical measurement and computational analysis system. Instead of coaches physically recording data, optical sensors capture ball motion parameters, and computational algorithms process this data to automatically determine hit outcomes, substituting human manual labor with automated technological systems.
3Reliability
If the system incorporates multiple performance metrics (fielder stats, environmental factors), then hit outcome prediction accuracy improves, but system complexity increases
Solution Approach 1:
The computational module serves multiple functions: it processes ball motion parameters from optical sensors, integrates system performance metrics (fielder reaction time, running speed, arm strength), incorporates environmental factors (wind, humidity, temperature), and queries the database to determine hit outcomes. This multi-functional approach consolidates diverse data processing tasks into a single universal module, managing complexity through functional integration.
Solution Approach 2:
The system performs preliminary action by pre-compiling databases of real-life pitch outcomes and pre-establishing performance metric profiles for various fielders and environmental conditions. This preliminary preparation allows the computational module to quickly match measured ball parameters against pre-analyzed scenarios, reducing real-time processing complexity while maintaining high prediction accuracy.
Data Source
AI summary
Pitching simulator systems and methods are provided that are used to predict real-life outcomes for a real-life pitch. Predicted real-lift outcomes can be outcomes such as a hit (e.g., groundout, double, home run, etc.), strikeout, etc., and can be made in real-time. More specifically, the systems and methods can identify pitch metrics corresponding to the real-life pitch, cross-reference data in the form of system performance metrics that can related to data about batters, fielders, and/or an environment, and then calculate a virtual pitched ball outcome based on a combination of the pitch metrics and the system performance metrics. Computer programs capable of being used in such systems and methods, as well as other systems and methods, are also provided.